Road geometry modelling is very useful for three dimensional (3D) map creation and 3D terrain identification along with feature and obstacle detection in environments, each of which may facilitate autonomous vehicle navigation along a prescribed path. Traditional methods for 3D modelling of road geometry and object or feature detection are resource intensive, often requiring significant amounts of human measurement and calculation. Such methods are thus time consuming and costly. Exacerbating this issue is the fact that many modern day applications (e.g., 3D mapping, terrain identification, or the like) require the analysis of large amounts of data, and therefore are not practical without quicker or less costly techniques.
Some current methods rely upon feature detection from image data to perform road terrain detection, but these methods have deficiencies. For instance, some systems designed for terrain and feature detection around a vehicle exist, but may be unreliable. Further, the reliability of feature detection may not be known such that erroneous feature detection or lack of feature detection may adversely impact autonomous or semi-autonomous driving. Over-estimating the accuracy of feature detection may cause accuracy issues as object locations may be improperly interpreted as accurate when they are actually inaccurate, while under-estimating accuracy may lead to inefficiencies through overly cautious behaviors. Further, features may change over time and autonomous vehicles need to be able to properly detect and interpret features over varying states of decay.